DeepMind has recently announced a fresh collaborative partnership with the UK’s health service, with plans for the artificial intelligence firm to develop machine learning technology to research breast cancer.

DeepMind, a Google subsidiary, is perhaps best known for successfully building AI that is now better than humans at the ancient game of Go. But in recent months – when attempting to apply this tech to serious healthcare issues – it has been on the sidelines of a data breach storm.

In July, DeepMind’s collaboration with London’s Royal Free hospital led to the NHS trust violating the UK’s data protection laws.

The Information Commissioner’s Office (ICO) found that Royal Free’s decision to share 1.6m personally identifiable patient records with DeepMind for the development of Streams – an automated kidney injury detection software – was “legally inappropriate”. DeepMind wasn’t directly criticised by the ICO.

Personal records included patients’ HIV-positive status, as well as details of drug overdoses and abortions. Royal Free’s breach generated considerable media attention at the time, and it means that DeepMind’s latest partnership with an NHS trust will be scrutinised carefully.

It will be working with Cancer Research UK, Imperial College London and the Royal Surrey NHS trust to apply machine learning to mammography screening for breast cancer. This is a laudable aim, and one to be taken very seriously, given DeepMind’s track record. London-based DeepMind emerged from academic research, assisted by Google’s deep pockets. It is now owned by Google’s parent company Alphabet.

Mind reader

DeepMind appears to have learned from the Royal Free data breach, having “reflected” on its own actions when it was signed on to work with the trust. It said that the breast cancer dataset it will receive from Royal Surrey is “de-identified”, which should mean that patients’ personal identities won’t be shared.

Given DeepMind’s continued collaboration with the NHS on a range of research, citizens are rightly concerned about how private corporations might exploit the data they have willingly shared for publicly funded work.

Few details about the Royal Surrey research project – which is in the early stages of development – have been released, but it’s likely that DeepMind will focus on applying deep neural networks for scanning mammogram images to automatically identify signatures of cancerous tissue. This approach would be similar to its Moorfields Eye Hospital project, where DeepMind is building automated machine learning models that can predict macular degeneration and blindness from retinal scans.

Human nature

From my own experience in applying data analytics to medical diagnostics in neurology, I know that – even if things go well for DeepMind and it manages to build a machine learning model that is excellent at detecting the early signs of breast cancer – it might well face a more practical problem in its application to the real world: interpretability.

The practice of medicine today relies on trust between two humans: a patient and a doctor. The doctor judges the best course of treatment for a patient based on their individual clinical history, weighing up the relative pros and cons of the different options available. The patient implicitly trusts the doctor’s expertise.

If a doctor or patient fails to understand and communicate the rationale behind a recommendation, it might be very difficult to convince either to adopt it. And no machine learning algorithm is likely to be perfect. Both false positives and negatives are of great consequence in the healthcare context.